Real-Time Environment Monitoring System

An end-to-end IoT and Data Visualization project that simulates real-time environmental data (temperature, humidity) using virtual sensors, publishes it over MQTT, stores it in InfluxDB, and visualizes it using Grafana dashboards with real-time alerts.

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🚀 Features

• Simulated IoT sensor data using Python
• MQTT protocol for real-time data transfer
• Node-RED flow for parsing, formatting, and routing data
• InfluxDB (Docker) for time-series data storage
• Grafana dashboards for real-time insights
• Threshold-based alerts using Grafana Alerting
• Flux queries for advanced analytics

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🧱 Architecture Overview

[ Python Sensor Simulator ]
          |
       MQTT Broker (Mosquitto)
          |
       Node-RED
        |- JSON Parsing
        |- Data Formatting (InfluxDB Line Protocol)
        |- Extract Fields
        |- Store in InfluxDB
        |- Debug + Charts
          |
       InfluxDB (Docker)
          |
       Grafana (Docker)
        |- Dashboards
        |- Alerts (Rain Prediction, Heatwave, Coldwave)

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📦 Repository Structure

real-time-environment-monitoring/
├── sensor_simulator.py       # Python script for simulating sensors
├── node_red_flow.json        # Exported Node-RED flow
├── influxdb-docker-compose.yml # Docker Compose for InfluxDB and Grafana
├── grafana_dashboard.json    # Grafana dashboard JSON
├── alerts_flux_queries.md    # Flux alert expressions
├── README.md                 # Project overview and setup guide

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⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/your-username/real-time-environment-monitoring.git
cd real-time-environment-monitoring

2. Start Docker Services

docker-compose -f influxdb-docker-compose.yml up -d

3. Import Node-RED Flow

• Launch Node-RED at http://localhost:1880
• Import node_red_flow.json
• Deploy the flow

4. Run the Sensor Simulator

python sensor_simulator.py

5. Set Up Grafana

• Access Grafana at http://localhost:3000
• Login (default: admin/admin)
• Add InfluxDB as a data source
• Import grafana_dashboard.json for visualizations

6. Alerts & Analytics

• Navigate to Alerting → Manage
• Use Flux queries from alerts_flux_queries.md to set up:
  • 🌧 Rain prediction (based on humidity)
  • 🔥 Heatwave (high temperature)
  • ❄️ Coldwave (low temperature)

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📊 Metrics Visualized

• Live Temperature Trends (line chart)
• Humidity Levels Over Time
• Last Recorded Sensor Data (single stat)
• Dynamic Sensor ID Switching (based on topic)
• Alert thresholds with visual markers

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🛠 Technologies Used

• Python — Sensor simulation
• Node-RED — Workflow automation & parsing
• MQTT (Mosquitto) — Messaging protocol
• InfluxDB — Time-series database
• Grafana — Dashboards and alerts
• Docker — Environment setup and orchestration

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📌 Notes

• Each simulated sensor has a dynamic ID (kiit_<random>), which is handled in Node-RED and InfluxDB tagging automatically.
• Timestamp conversion is handled using timestamp * 1000 for Influx compatibility.
• Alerts use Grafana-managed rules based on Flux expressions.

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🧪 Future Improvements

• Add air quality and pressure sensors
• Enable long-term trend analysis
• Integrate ML model for weather classification
• Mobile-friendly Grafana dashboards

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📝 License

This project is licensed under the MIT License. See LICENSE for more details.